poverty.data: Poverty data for testing the _BNPMIXcluster_ package

In christianu7/BNPMIXcluster: Bayesian Nonparametric Model for Clustering with Mixed Scale Variables

Poverty data for testing the BNPMIXcluster package

Description

Poverty indicators observed in Mexico for 2014.

The original data is available in the file "R_2014.zip" from CONEVAL's website: http://www.coneval.org.mx/Medicion/MP/Paginas/Programas_BD_10_12_14.aspx

(download zip file directly from: http://www.coneval.org.mx/Medicion/MP/Documents/Programas_calculo_pobreza_10_12_14/R_2014.zip )

This data frame presents indicators aggregated by household. The aggregation was done by the authors according with code in section Examples.

Usage

poverty.data

Details

poverty.data is a data frame with 58121 rows and 13 variables, with the following columns:

proyecto

Data source identifier (1=MCS, 2=ENIGH)

folioviv

Household identifier level 1

foliohog

Household identifier level 2

ict_norm

(continuous) Total income in the household (in Mexican pesos).

ic_ali

(binary) Indicator for deprivation to feeding: 1-yes,0-no

ic_asalud

(binary) Indicator for deprivation to health services: 1-yes,0-no

ic_cv

(binary) Housing quality: 1-bad, 0-good

ic_rezedu

(binary) Indicator for education backwardness: 1-yes,0-no

ic_sbv

(binary) Indicator for deprivation to basic public services: 1-yes,0-no

ic_segsoc

(binary) Indicator for deprivation to social security: 1-yes,0-no

niv_ed

(categorical, ordered) Maximum educational level in the household: 0-incomplete primary: 1-incomplete secondary, 2-complete secondary or more

tam_loc

(categorical, nominal) Size of locality according to the number of people living there: 1-(100000, ∞), 2-[15000, 100000), 3-[2500, 15000), 4-[0, 2500)

factor_hog

Expansion factor for the household, according to the complex survey design.

See Also

MIXclustering

Examples

##### Generates poverty.data using the original data from CONEVAL's website #####

## Not run: 
# step 1:
#     Download and unzip the file "R_2014.zip"
#     available in:
#     http://www.coneval.org.mx/Medicion/MP/Documents/Programas_calculo_pobreza_10_12_14/R_2014.zip

# step 2:
#     extract and read the csv file "pobreza_14.csv"

coneval.poverty.data <- read.csv("pobreza_14.csv", na.strings=c("NA",""))

# step 3:
#     Execute the following code...

var_id <- c("proyecto","folioviv","foliohog","numren")
for(i in match(var_id,colnames(coneval.poverty.data)) ){
     coneval.poverty.data[,i] <- formatC( x=as.numeric(coneval.poverty.data[,i]),
                                          width=max(nchar(coneval.poverty.data[,i])),
                                          format="f",flag="0",digits=0
                                          )
}

# normalizing the continuous variable for income #
b <- quantile(coneval.poverty.data$ict,probs=0.01)
coneval.poverty.data$ict_norm <- log(coneval.poverty.data$ict+b)

# Aggregating data at household level
Y_names <- c("ict_norm",
             "ic_ali","ic_asalud","ic_cv",
             "ic_rezedu","ic_sbv","ic_segsoc",
             "niv_ed","tam_loc")
agg_form <- as.formula( paste( "cbind(",paste(c(Y_names,"factor_hog"),collapse=",") ,")",
                               "~proyecto+folioviv+foliohog"
                             )
                      )

poverty.data <- aggregate(agg_form,FUN="max",data=coneval.poverty.data)


## End(Not run)

christianu7/BNPMIXcluster documentation built on Sept. 10, 2022, 11:40 p.m.